AIMC Topic: Learning

Clear Filters Showing 1411 to 1420 of 1476 articles

Learning From Peers' Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading, Eye Tracking, and Machine Learning.

Cognitive science
Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized fee...

Computing of temporal information in spiking neural networks with ReRAM synapses.

Faraday discussions
Resistive switching random-access memory (ReRAM) is a two-terminal device based on ion migration to induce resistance switching between a high resistance state (HRS) and a low resistance state (LRS). ReRAM is considered one of the most promising tech...

A new ensemble residual convolutional neural network for remaining useful life estimation.

Mathematical biosciences and engineering : MBE
Remaining useful life (RUL) estimation is one of the most important component in prognostic health management (PHM) system in modern industry. It defined as the length from the current time to the end of the useful life. With the rapid development of...

Semisupervised category learning facilitates the development of automaticity.

Attention, perception & psychophysics
In the human category of learning, learning is studied in a supervised, an unsupervised, or a semisupervised way. The rare human semisupervised category of learning studies all focus on early learning. However, the impact of the semisupervised catego...

[Artificial intelligence for future MD].

Giornale italiano di nefrologia : organo ufficiale della Societa italiana di nefrologia
Health care workers need artificial intelligence. Artificial intelligence is a set of studies and techniques that tend to the realization of machines, which solve complex problems automatically, simulating or emulating human intelligence activities. ...

A review of abstract concept learning in embodied agents and robots.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
This paper reviews computational modelling approaches to the learning of abstract concepts and words in embodied agents such as humanoid robots. This will include a discussion of the learning of abstract words such as 'use' and 'make' in humanoid rob...

Biosignal Data Augmentation Based on Generative Adversarial Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this paper, we propose a synthetic generationmethod for time-series data based on generative adversarial networks (GANs) and apply it to data augmentation for biosinal classification. GANs are a recently proposed framework for learning a generativ...

Unsupervised Phase Learning and Extraction from Repetitive Movements.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Phase extraction from repetitive movements is one crucial part in various applications such as interactive robotics, physical rehabilitation, or gait analysis. However, pre-existing automatic phase extraction techniques are specific to a target movem...